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Record W4391435412 · doi:10.3899/jrheum.2023-1221

Has the Time Come?

2024· editorial· en· W4391435412 on OpenAlexvenueno aff
Yukinori Takagi

Bibliographic record

VenueThe Journal of Rheumatology · 2024
Typeeditorial
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatologySjögren syndromeDiseaseInternal medicineLacrimal glandDermatologyPathologyAutoimmune disease

Abstract

fetched live from OpenAlex

Sjögren syndrome (SS) is a chronic inflammatory autoimmune disease that primarily affects the lacrimal and salivary glands. Glandular tissue destruction progresses with disease progression, severely impairing the patient’s quality of life. The disease is not limited to the glandular tissue alone, with a high risk of various extraglandular manifestations and malignant lymphomas.1,2 Nearly a century after Dr. Henrik Sjögren’s report in 1933, SS has seemingly reached a significant turning point.3 In recent years, several editorials have suggested redefining SS as Sjögren disease and eliminating the distinction between the primary and secondary forms; similar opinions were raised by SS patient groups at the 15th International Symposium on Sjögren’s in 2022.4,5 In addition, the classical idea that SS affects middle-aged women is certainly overdue for a revision, especially now that childhood SS is receiving increasing attention.6 However, most cases of childhood SS do not meet the 2016 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) criteria, which are the most recent classification criteria for SS.7-9 This may be because imaging examinations have not been included in the classification criteria since the publication of the 2012 ACR criteria.10 Since the publication of the 2012 ACR criteria, reports on the usefulness of imaging examinations in diagnosing SS have increased substantially. Salivary gland ultrasonography (US) has contributed to this trend. US was first used to diagnose SS in the late 1980s, and … Address correspondence to Assoc. Prof. Y. Takagi, Department of Radiology and Biomedical Informatics, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1, Sakamoto, Nagasaki 852-8588, Japan. E-mail: yuki{at}nagasaki-u.ac.jp.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.056
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.264
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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